Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression

• URL: http://arxiv.org/abs/2606.06567v1
• Date: Thu, 04 Jun 2026 17:29:56 GMT
• Title: Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
• Abstract summary: Lack of support for uncertainty quantification limits its adoption in real-world decision processes.<n>This survey is the first to clearly address this issue, with the objective of introducing essential UQ concepts and reviewing the current literature on UQ in SR.<n>Despite its importance, UQ in SR is still underexplored, which motivates further research into reliable UQ methods for SR.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in real-world decision processes. In regression analysis, UQ provides important information about the model reliability, which can both help to avoid overfitting by accounting for uncertainty in the data, and provide insights for decision-making. This survey is the first to clearly address this issue, with the objective of introducing essential UQ concepts and reviewing the current literature on UQ in SR, which can be broadly organized into three research directions: frequentist, Bayesian, and model selection. Despite its importance, UQ in SR is still underexplored, which motivates further research into reliable UQ methods for SR.

Related papers

• Benchmarking Uncertainty Calibration in Large Language Model Long-Form Question Answering [7.1559850008795385]
  Large Language Models (LLMs) are commonly used in Question Answering (QA) settings.<n>Existing UQ approaches remain weakly validated in scientific QA.<n>We introduce the first large-scale benchmark for evaluating UQ metrics in reasoning-demanding QA.
  arXiv  Detail & Related papers  (2026-01-30T20:02:34Z)
• Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering [29.4458902836278]
  We introduce a task-agnostic, token-level uncertainty measure defined as the cross-entropy between the predictive distribution of the given model and the unknown true distribution.<n>We derive an upper bound for uncertainty and show that it can be interpreted as semantic feature gaps in the given model's hidden representations.<n>We apply this generic framework to the contextual QA task and hypothesize that three features approximate this gap: context-reliance, context comprehension, and honesty.
  arXiv  Detail & Related papers  (2025-10-03T02:09:25Z)
• Uncertainty Quantification for Regression using Proper Scoring Rules [76.24649098854219]
  We introduce a unified UQ framework for regression based on proper scoring rules, such as CRPS, logarithmic, squared error, and quadratic scores.<n>We derive closed-form expressions for the uncertainty measures under practical parametric assumptions and show how to estimate them using ensembles of models.<n>Our broad evaluation on synthetic and real-world regression datasets provides guidance for selecting reliable UQ measures.
  arXiv  Detail & Related papers  (2025-09-30T17:52:12Z)
• Uncertainty Quantification in Retrieval Augmented Question Answering [45.573346610161195]
  We propose to quantify the uncertainty of a QA model via estimating the utility of the passages it is provided with.<n>We train a lightweight neural model to predict passage utility for a target QA model and show that while simple information theoretic metrics can predict answer correctness up to a certain extent, our approach efficiently approximates or outperforms more expensive sampling-based methods.
  arXiv  Detail & Related papers  (2025-02-25T11:24:52Z)
• UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models [55.22048505787125]
  This paper contributes a comprehensive dataset, called UNK-VQA. We first augment the existing data via deliberate perturbations on either the image or question. We then extensively evaluate the zero- and few-shot performance of several emerging multi-modal large models.
  arXiv  Detail & Related papers  (2023-10-17T02:38:09Z)
• Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions [70.70725223310401]
  This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models. The experimental results demonstrate that the proposed evaluation method effectively analyzes the robustness of VQA models.
  arXiv  Detail & Related papers  (2023-04-06T15:32:35Z)
• Toward Unsupervised Realistic Visual Question Answering [70.67698100148414]
  We study the problem of realistic VQA (RVQA), where a model has to reject unanswerable questions (UQs) and answer answerable ones (AQs) We first point out 2 drawbacks in current RVQA research, where (1) datasets contain too many unchallenging UQs and (2) a large number of annotated UQs are required for training. We propose a new testing dataset, RGQA, which combines AQs from an existing VQA dataset with around 29K human-annotated UQs. This combines pseudo UQs obtained by randomly pairing images and questions, with an
  arXiv  Detail & Related papers  (2023-03-09T06:58:29Z)
• Towards Clear Expectations for Uncertainty Estimation [64.20262246029286]
  Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML) Most UQ methods suffer from disparate and inconsistent evaluation protocols. This opinion paper offers a new perspective by specifying those requirements through five downstream tasks.
  arXiv  Detail & Related papers  (2022-07-27T07:50:57Z)
• Abstractive Query Focused Summarization with Query-Free Resources [60.468323530248945]
  In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting.
  arXiv  Detail & Related papers  (2020-12-29T14:39:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.